If you live in NSW, you might have noticed it’s been pretty dry lately. In Sydney we’ve had our driest September on record, and the last time we had real rain was two whole months ago, the longest spell on record. After a very wet March, April-September has been the driest for Sydney in over a decade.

In the absence of El Niño, we look to other explanations, like the very high pressure in June. And for the east coast, it feels like we just haven’t had any ECLs. But is that true?

Conveniently, NCEP in the USA provides grids of “reanalysis” pressure data almost in real time, so I ran my tracking scheme and had a look! During April-September, which is the main ECL season (as well as the period it’s been so dry), typically there are 28 days with a low pressure system somewhere in the ECL region. This year there were 24 – not that much different to normal.

All cyclones April-September 2017. Crosses mark strong systems

I know what you’re thinking – you would remember if that had happened! As it turns out, our standard ECL box extends pretty far east, and this year the systems were generally too far east (or south) to affect the coast.

Only 12 days this year had an system closer to the coast, the fewest since 2003. And only five were north of 38°S, the main one being a pretty weak low on the coast that contributed to rain during early June.

In fact, they were all pretty weak. Only four days had an ECL reach a strong threshold anywhere in the domain, and just one in the closer region (and it was very far south).

Usually there are 12 days with a strong ECL; last year there were 18 days, including 15 with a strong ECL near the coast and a pretty severe one in June! Amusingly, this means that while this year had the fewest strong ECLs since 1994, last year had the most since 1990, really showcasing how variable ECLs are!

Number of April-September ECLs from the NCEP reanalysis.

But why?

Well, I'd like to tell you. Unfortunately, we actually don't have a very good understanding of why and how ECLs vary from year to year. There's some evidence that they're more likely in La Niña years, especially the ones with very heavy rain and flooding - but generally their relationship with the big climate drivers is pretty weak, with some very significant ECLs during big El Niño years like 2015.

While there is some relationship with changes in pressure patterns such as SAM, this year hasn't been that much different to the last couple, which had heaps of ECLs. And that really high pressure from June didn't really continue into the rest of winter.

So I actually have no idea. It could just be random chance, or maybe there are climate patterns we just don't understand yet. But it would be an interesting thing to know!

]]>Fri, 08 Sep 2017 04:26:38 GMThttp://acaciapepler.weebly.com/blog/new-paper-do-mountains-matter-for-east-coast-lowsI was lucky enough to have two papers published in the last month. The second was the final part of my PhD, so publishing this paper is the real end of my studies. It’s also probably the most challenging paper I wrote, so I thought I would talk about the process a bit.

A long journey

From the beginning, understanding the relationship between ECLs and the Great Dividing Range next to the coast was going to be one of the major points of my PhD. My first model simulations were done three years ago, when I was playing with changing topography for a single month in my regional model, and my first presentation about it was all the way back in June 2015.

So why did it take so long to publish anything? Part of it was getting distracted by other shiny things to study, but it was also due to my growing understanding of what we should and shouldn’t do with regional models - that is, models that are run at high resolutions but over small areas of the globe.

My early simulations were for a single month, which doesn’t really tell you much about changes in the frequency of ECLs. So the next step was running the model over 2 years, just like I did with changes in sea surface temperatures. But after doing all that, I got asked some really important questions by my supervisors.

Simply, what I had been doing for sea surface temperatures was “nudging” the model fields in the upper atmosphere to be more like the global observations. This is good to make sure the broader atmosphere is the same, so we can compare individual events. But unlike oceans, mountains extend well up into the atmosphere, which means that this “nudging” is actually stopping the model from properly incorporating the changes.

So, more model simulations, that had to run for a lot longer. As part of this, I made a mistake setting up one of my model simulations which I didn't realise til it was done, so I had to do it all over again, wasting another month.

… about a month before I got my thesis results, the paper was rejected. :(

It was too messy - I had tried to include all my different types of simulations, and the results just weren’t very clear. In the end I had to throw out almost everything - all of those 2-year simulations, the extra runs I did because I had started them wrong, everything except a couple of my long simulations. All those thousands of hours of supercomputer time and countless hours of analysis, for nothing. It was quite depressing, especially since I hadn’t had a rejected paper in half a decade.

But I got over my sadness and rewrote the whole paper from scratch, to create something much clearer. It was hard, but worth it when I resubmitted and the reviewers accepted it with only minor comments. Hooray!

So, what’s the moral of the story?

Put more thought in the best way to do your model simulations before you start, so you don’t have to redo them all

Just because you spent a lot of time on something, doesn’t mean it should be in the paper. Learn to let go

As always, academia is about persistence. It may feel depressing when a paper gets rejected, but it’s not a reflection on you and there’s always another chance

But what was the paper about?

Climate models tend not to have enough ECLs near the coast, so I wanted to know how important the Great Dividing Range is, since it basically doesn’t exist in a global model that has 200 km between points. So I ran my regional model over Australia & surrounds with the height of all the land set to 0m.

And what did I find? Topography actually isn’t as important as everyone thought. Removing it means we get fewer ECLs coming from the Bass Strait, especially in spring, but the number that form near the coast actually increases! There’s also no change in the number of strong events, although the wind speeds and rain on the coast are lower because they’re not being forced up by the mountains.

Change in average rain rate and wind speed for ECLs within 500km from the coast when topography is removed. Plot shows a radius of 500km from the centre and is oriented so north is at the top

That seems like a lot of work for not much result, but it’s actually quite important to know that we shouldn’t focus so much on the Great Dividing Range when it comes to ECLs - it’s much less important than the warm oceans near the coast. So future scientists (or future me) can move on to other questions, like what would happen if we wiped out New Zealand? I think that would be super interesting!

]]>Sat, 19 Aug 2017 00:00:44 GMThttp://acaciapepler.weebly.com/blog/new-paper-ecls-in-satellite-dataIt's been a while since I wrote a blog, but it's always good to talk about new research. In the limbo stage between finishing my PhD and finding a job, one of my supervisors luckily had the funds to employ me as a research assistant for a few months, where I worked on this project. I started work on it at the end of January, so it's pretty cool that it's published online already!Example of what an average ECL looks like from reanalysis pressure data

One of the things I've mentioned before is that there's no perfect database of past East Coast Lows, which would be a handy thing to have to look at things like trends, variability, or whether climate models do a good job. The main way we identify ECLs is by taking computer code that's good at identifying areas of low pressure, and applying it to some sort of gridded pressure data.Since we can't get gridded pressure observations from satellites, what we use are called reanalyses. These are basically a weather forecast model that takes in all of the observations from around the world to make the best possible guess of what the atmosphere currently looks like.

Average ECL wind pattern from satellite data

There are about half a dozen of these reanalyses, and they all use different models. And the tricky thing is that if you smooth them all out to the same resolution - about 250 km between each grid point - they all kinda show the same results when you look for ECLs using your algorithm. But when you use grids that are more like 50 km instead, some reanalyses seem to have more than double the number of cyclones that others do!

So this paper is an attempt to see which reanalysis is actually the most correct, using satellite data. Because while we don't have global observations of pressure, we do have satellite observations of rainfall and winds. And we know that a "cyclone" should have "cyclonic" winds - which in the southern hemisphere basically means clockwise, spiraling into that central low point. So we took the satellite winds for a database of "known" ECLs to find some parameters that an ECL should have, and then looked at what the satellite winds showed for a whole bunch of reanalyses.

What we found was that there was a really simple metric we could use to check if an identified ECL was a "real" cyclone with cyclonic winds, and using this made the reanalyses a lot more similar. The one reanalysis that had too many ECLs, had a lot of systems which weren't really cyclones, and looked a lot more like a cold front. Conveniently, the "best" reanalysis for ECLs was the European one, which is already the most popular for ECL studies. This helps us get a better handle on which dataset to use for evaluating climate models.

]]>Thu, 30 Mar 2017 10:39:41 GMThttp://acaciapepler.weebly.com/blog/how-i-write-a-scientific-paperWhile waiting for my examiner’s results and to find a job, I’ve been doing some research assistant work for one of my supervisors, which is a great way to keep having something to do (and keep having money!)

We’re now at the stage where it’s time to start to think about turning the work I’m doing into a scientific paper, so I thought I would talk about how I tend to write papers. A lot of people have written about this before, and I highly recommend Sophie’s advice (she's actually a journal editor).

Step 1. Have some results

This step perhaps goes without saying, but before you start to write a paper you need to have something to write about!

Some people start with a brilliant idea for a paper that just hasn’t been written yet. For me, it usually starts with “I wonder…”. In this case, we knew that different reanalyses have different representations of East Coast Lows, so we wondered whether we could use satellite rainfall or wind data to assess which one is the best.

So that’s followed with a lot of writing code to analyse data, make a mountain of figures, and look at a whole bunch of different numbers. And at some point, you realise that some of what you’ve found is interesting, and is worth sharing with the world.

Step 2. Decide what the story is

The rookie mistake I made when I was writing my first paper, and that I think a lot of other young scientists do too, is wanting to show all your work. You’ve done so much work, and there are so many little things that are interesting, and you want to write about it all!

Unfortunately, this usually ends up with a paper that’s a bloated mess - paragraphs full of numbers, page after page of repetitive tables or figures. When it’s hard to understand what you’re trying to say, readers get really bored.

What you want is a tight, short paper that tells a single story, with only as many figures and as much detail as you need to convey the story. This means figuring out what your most important results are, and what you need to say in order to make your results clear.

In theory, this should also be the time when you decide what journal to target, because that helps you know how you should structure it. Although sometimes I don’t actually do this til step 4, oops.

Step 3. Write an outline

This isn’t for everyone, but I love writing outlines.

I start with the major sections - the different parts of the results/story, and what order I think they should go in. Then I fill it in with a few dot points about what each section should cover, and start putting in the main figures (these are not in a final form, just to start the planning.

From there, I will send the outline around to the co-authors to get their opinions. That way we can make sure everyone’s on the same page and discuss what results are unnecessary and what extra analyses to do, before wasting time writing something that may need to be totally rewritten later.

(As for who to have as coauthors? I tend to be on the generous side of things, because I’d rather have more people, and more eyes to find mistakes, than go fewer and take the chance of insulting someone. But everyone’s equation is different)

Step 4. Start filling in the details

By this point, my outline is usually like 5 pages long, from all the figures and dot points and tables. So the next step is to start changing those dot points into paragraphs. I can’t really give much guidance on how to do that part, I’m afraid. Once I have the scaffolding of the outline, expanding dot points into paragraphs is relatively straightforward for me.

I’ll typically write the results first, then the methods and introduction, then the conclusions, and leave the abstract to the very end. Then I’ll print the whole thing out and edit it with a pen, to really find all those places where the writing doesn’t make sense, before sending a draft around to coauthors. And iterate until everyone’s happy.

Step 5. Clean everything up

This is more important than you think it is. Do your spell checks, and check that the reference list is all consistent. Make sure the paper is formatted in the journal’s style, and that it’s not too long or with too many figures.

Go through the figures and make sure that they’re clear (and any text is readable), that they have labels if they need to, that they don’t use the rainbow colour scheme, and that they convey the story you’re trying to tell easily. For bonus points, it can be good to make sure as many as possible look okay if printed in black & white.

If you’re anything like me, and your code when you’re analysing data is a bit all over the place, now is a good time to clean up your code and make sure the bits you need to actually produce the figures and results in the paper are in one place and hopefully clear & commented enough that when you come back to the paper you’ll easily be able to follow & recreate what you did. This is more and more important with the current movement towards transparent & reproducible scientific code.

Step 6. Submit... and wait

Usually it takes 2-3 months for a paper you’ve submitted to come back. Most of the time you’ll get asked to do either minor or major revisions. This usually means explaining things reviewers didn’t understand and doing more analyses to make the paper stronger (or make sure your results say what you think they do.

I’m not going to go into how to respond to comments, but Sophie has some great tips. Just remember to thank the reviewers, because they usually make the paper stronger. And remember to review other papers & do your best to be both kind and rigorous, because otherwise the system crumbles.

And sometimes you’ll get a reject and resubmit, or just a flat reject. This can be disheartening - after all, you put so much work into this! But about 30% of papers are rejected, and it happens to all of us - I had a paper rejected just a couple of months ago. But usually, this just means you need to rethink how you designed your paper (or where you submitted it), and give it another go. Re-working that rejected paper is on my to-do list for April, now that I’ve submitted my responses to my thesis examiners.

Step 7. Celebrate!

If all works well, after a couple of rounds of review, your paper will be published, and you’ll have a moment of excitement… before starting to work on the next one. :)]]>Sat, 11 Mar 2017 00:05:55 GMThttp://acaciapepler.weebly.com/blog/on-the-academic-job-hunt-and-the-two-body-problemIt's been two months since submitting my PhD. In some ways, life has continued as it always has - working part time at the Bureau, working for one of my PhD supervisors as a research assistant on a project closely related to my PhD, and doing bits and pieces of journal reviewing and collaborations in my (now unpaid) spare time. Hopefully I'll hear back from my examiners in the next month or so, so I can finally call myself a Doctor.

At the same time, I've also been looking for jobs, so I can know what I'll be doing in the second half of the year. I've only applied for a few positions so far - I've managed to get interviews for all of them but haven't received any job offers yet, so I'm still looking.

I don't think I realised before I started how difficult the academic job hunt is. It's a global market, and across the globe there are, say, 5 climate-ish jobs advertised per day, the vast majority of which are probably too different from what I do to have a chance at - glacier modelling, or atmospheric chemistry, or deep ocean stuff. The more niche you get, the fewer jobs there are (for instance, there have been precisely two jobs advertised that relate to midlatitude/extratropical cyclones in the past nine months). This is why most people end up changing research areas after finishing their PhD.

The other problem is the well-known ﻿"two body problem". There are a lot of couples where both are academics, and finding a place where both can get academic jobs within an hour of each other is incredibly difficult, leading to a lot of people leaving the field.

But a related struggle, in my opinion, is having a partner who has an important non-academic career. My husband does not work in academia, but in business/banking. This means that if we stay in a big city like Sydney (or Melbourne), he will have zero problems finding a job. Yay! However, it means he needs to work in big cities, so we can't move to any of the wonderful academic institutions around the world that are based in small towns or regional centres.

And while English is currently the language of science, this isn't true for most other fields. We've been learning German to open up our options, but we're far from fluent yet. And many excellent academic jobs are advertised in places like France or Norway, where he wouldn't be able to get a job for a long time because he would need to learn the language first. Or maybe not be able to get a job at all, lacking any local references or connections. So while we have an advantage over academic two-body couples in English-speaking countries, we have a disadvantage in the rest of the world. And let's face it, the UK and US seem to be particularly bad places for scientists to work at the moment.

Unfortunately, for some reason scientific careers still strongly encourage "mobility", so staying at my current institution in Sydney (which I adore) apparently looks bad on the CV. This is probably one of the small things contributing to the "leaky pipeline", because women are more likely to have a partner that also has a career and thus less likely to be mobile. Maybe one day the scientific establishment will realise that most people can't just up and move their family wherever the work is and expect their spouse to just deal with it.

But for now, we're still looking for the perfect job, that allows my career to develop without forcing him to sacrifice his. I guess we'll have to wait and see.]]>Thu, 09 Feb 2017 11:38:37 GMThttp://acaciapepler.weebly.com/blog/dealing-with-failureThere are many skills that are necessary to become a successful academic - big ideas, programming and analysis skills, communication, grant applications, etc. But I’m beginning to realise that one of the most important things is tenacity and persistence - the ability to fail again and again and keep trying.

In some ways, an academic career involves constant failure, with disappointing or useless results, rejected papers, unsuccessful grant applications and the like all very common. Every successful scientist seems to have a whole range of rejections they can point to, especially when the ratio of PhD graduates to permanent jobs is so high and grant success rates on the order of 15%.

I’ve been thinking about failure, and how to deal with it, a lot this week. After putting a lot of work into what I still think was a pretty good application for a Marie Sklodowska-Curie Award, I got very positive comments but missed out on funding. And while I knew the success rates were low, I’d let myself get my hopes up, so it was very disappointing. Meanwhile, we’ve been celebrating the successes of some of the superstars in our field, which just heightens the contrast, when I now don’t know what I’ll be doing in three months time.

Being successful is a lot about being the best you can, but a huge proportion remains about luck. So missing out on the grant was a very normal introduction to the world of academia. But it’s hard, emotionally, looking ahead and seeing a future of failure after failure in the pursuit of the occasional success. It requires a certain amount of emotional resilience, and I’m beginning to wonder if I have it.

I was one of those kids who things came too easily to, so my default response to failing at something is to want to avoid it in future. But in order to have the career I want, I need to learn how to embrace the failures and keep on coming.

So how to handle it?

Luckily, I’ve spent this week at the AMOS conference, one of my favourite times of year, with compliments on my talk and research helping to remind me that yes, I am good at what I do, and that at least Australian scientists value me even if Europe doesn’t. Someone once suggested keeping a record of interactions like that to help get you through the sad times, and it might not be a bad idea.

But the main way to deal with failure is to just keep trying and putting yourself out there, so you can finally get the successes that make it all worthwhile. And maybe grow a thicker skin in the process. So I had a job interview tonight, and hopefully they liked me. Otherwise, I just have to remind myself how lucky I am to even have got this far, and appreciate what I have achieved even if I can’t achieve everything I want to.

If anyone has any recommendations on how to handle failure, I’d love to hear them.

]]>Wed, 25 Jan 2017 07:30:13 GMThttp://acaciapepler.weebly.com/blog/in-the-waiting-placeI submitted my PhD a couple of weeks ago, after just under three and a half years of research and study. I’m proud of what I’ve achieved, and of the fact that I've grown much more concise since my Master's thesis. Fingers crossed, the examiners will like it too, but I guess I’ll find out in a few months.

It’s exciting to have the PhD finished, after such a long time working towards it. It’s also a little anticlimatic, and scary, because a huge portion of my life is suddenly gone. And now I find myself in the waiting place - waiting to hear back about my PhD, and waiting to find out how I'll spend the next few years of my life. Hoping that someone out there likes me enough to employ me, or that I’m one of the lucky 10% whose grant applications are successful. It's tough, getting into a career in science.

Academia is a bit like a pyramid scheme. From the excellent PhD Comics, http://www.phdcomics.com/comics/archive.php?comicid=1144

It’s not great for your self-esteem, trawling job ads to look for something that fits your skills in a country that’s feasible, and then waiting to hear if you'll be interviewed or not. It’s depressing how few suitable jobs there seem to be, especially when you need to move a spouse with you. And the process of applying has made me think a bit about what makes someone an outstanding/great/employable scientist.

These days, one of the main things is papers. Luckily, I’m a relatively prolific writer who doesn’t find writing too arduous a task. But it's more complicated than that. On the one hand, a Professor recently complained that grants reward “quantity not quality”, which I guess favours people like me? On the other hand, I was reading an article today about how grants are all about having “outstanding” publications. And then there are all the other things you're told matter - how do people value media experience, or conference talks, or social media, or teaching? Is it really all just about who you know?

And then, there’s the question of research topic.

I study East Coast Lows because I live in Sydney, where they’re hugely important phenomena that have a big impact on people, and because when I started out they were surprisingly poorly understood, with a lot of questions needing to be answered. Studying a topic like this has a lot of advantages! It’s easy to explain to people of all backgrounds, and they’re interested in what I have to say. It gives me great opportunities for media and communication experience, as well as working with groups like state government agencies. It’s allowed me to build a strong network with colleagues within Australia, where I’ve kinda become the go-to person on this topic.

But the downside is that my PhD was very Australia-focused. While I can build relationships with other researchers studying cyclones, and met quite a few of them last year, by and large my results so far are mostly interesting to Australians. This means that, as long as I study them, I’ll never have a paper in the top journals like Nature and Science, unlike fellow studentswho are studying things a bit more globally applicable. And while my research questions are important, they’re not big global game-changing questions. So can any of my work truly be “outstanding”?

In the end, I’ll probably have an easier time convincing people of my worth if I stay within Australia than if I go overseas. I’m exploring all my options, because I just genuinely love research and science, especially climate extremes/variability/change, and could be happy in a lot of places. Although I’ll always have a soft spot for cyclones.

But for now, I’m stuck in the waiting place. At least I still have plenty of work to do for my ex-supervisor, to keep me occupied (and paid) while I wait to find out what my future will look like.

Update: My grant application was unsuccessful. While the comments were mostly really positive and the weaknesses really minor, that's not good enough with so many scientists competing for ever-shrinking pots of money. Back to the drawing board. :(

]]>Sun, 11 Dec 2016 04:49:00 GMThttp://acaciapepler.weebly.com/blog/what-did-i-do-in-2016Last year, I decided it might be nice to take a moment at the end of the year to reflect on what I'd achieved. It's two weeks from christmas, the year is almost through, so I thought I would do it again!

In many ways, 2016 was the year of outreach. I was an author on three articles for The Conversation, I was in an ECL explainer video for Fairfax Media, I did a long 30-minute interview for radio/podcast Beyond Zero Emissions, as well as other media around big events. I also did a lot better at keeping up with twitter and this blog, with at least one blog post every month (except for April, when I was overseas), while still maintaining the CCRC facebook page. For all this work I was awarded the CCRC prize for science communication & outreach this month, which was fantastic.

Travel-wise, I went to the ﻿AMOS﻿ conference in February and ARCCSS workshop in November like always, as well as the EGU conference in Vienna in April, which I followed up with a trip to Switzerland to visit universities there. I also became part of the Expert Team for Sector Specific Climate Indices, for which I travelled to Barbados and India to help run workshops on how to use software for analysisng & interpreting climate data, giving presentations about data homogenisation & drought.

As for science, my actual job? Well, I had four first-author papers published this year, with the final paper for my thesis submitted yesterday (yay!) I'm also a coauthor on another paper that got published and one in review, with other collaborations developing that may bear fruit in the new year. My thesis is on track to submit in early January, with only the final edits & checks left to do, and I wrote a grant proposal that I'm waiting to hear back from in the next month or two. Fingers crossed!

Another good year, although it's not always easy to see that when you're dealing with the dislike of Reviewer 2 or kicking yourself for making stupid mistakes when running your model and having to run it all again (my that was annoying). Hopefully 2017 brings me nice examiners who love my thesis, and some sort of awesome job so I can keep doing science. I guess we'll have to wait and see.]]>Sun, 27 Nov 2016 00:36:33 GMThttp://acaciapepler.weebly.com/blog/new-research-do-warm-sea-surface-temperatures-affect-east-coast-lowsAverage strength of the EAC (°C)

But how strong is this relationship? I’m using regional climate models to try to find out exactly how much our warm coastal waters matter to ECLs, which was just published in the Journal of Geophysical Research.

Total number of ECLs during 2007-2008 for each WRF simulation under different SST scenarios. Points/colours indicate the different WRF setups

What’s the impact of warmer SSTs?

To try to answer this, I did lots of runs of a regional climate model over Australia during the years 2007-2008, changing sea surface temperatures to remove or strengthen the East Australian Current. Unsurprisingly, with cooler sea surface temperatures, we get fewer ECLs, and warmer oceans mean more ECLs. The change is about 20%, for a change in ocean temperatures of just 1-2°C.

The interesting thing is that this seems to mostly affect the weak systems – with warmer oceans, it’s easier for an East Coast Low to develop, even when the conditions in the atmosphere aren’t as favourable. The upper atmosphere is really important for the big ECLs, which still happen even with cooler SSTs, although warmer SSTs do help ECLs grow a little bit more intense.

Why does it matter?

Sea surface temperatures in the Tasman Sea in Australia are warming faster than most other places in the world. And while we know that cyclones are shifting south and the conditions favouring ECLs are likely to decrease, there’s a lot more uncertainty right near the coast.

Since most of us live near the coast, and ECLs are really important for things like dam levels, it’s important to know what will happen to the ECLs that matter most. And if ocean temperatures are important, we might see different changes in the future, which we need to plan for.

What’s next?

This work is a start, since nobody’s really looked at this question since 1992. But there’s still a lot to know about how, for instance, the temperature changes projected by climate models will do to ECLs, or if these results hold true in more complex global/coupled models. We also want to know more about individual events – for instance, were the record warm sea surface temperatures this year important for the record-breaking rain from the ECL in June?

That's one of the fun parts of being a scientist – there’s always more to learn.

]]>Mon, 10 Oct 2016 23:26:37 GMThttp://acaciapepler.weebly.com/blog/how-strong-is-a-cycloneLast month, a severe extratropical cyclone hit southeast Australia, which is being called a 1-in-50 years event. The storm caused severe winds, heavy rainfall, and major damage, including a blackout that affected the whole state of South Australia.

So, how do we know it was a 1-in-50 year storm, and how can we tell how strong a cyclone is in general? Tropical cyclones have a cyclone intensity scale we talk about, and people have some idea of the difference between a category 1, 3 or 5 cyclone. This is based on the strongest winds around the cyclone centre, which isn’t always a perfect indicator of how large its impacts are.

But what about extratropical cyclones, like the one that hit Adelaide, or the East Coast Lows that I study? They can be a bit harder to pin down.

1. Central pressure

One of the easiest ways to think about how strong a cyclone is is the pressure in the middle of a cyclone, with deeper cyclones being stronger. During the Adelaide cyclone, Bureau mean sea level pressure charts gave it a minimum central pressure of 973.3 hPa, which sounds pretty low.

Mean Sea Level Pressure chart for 1600EST on 28 September 2016. All pressure charts are from the Australian Bureau of Meteorology

MSLP chart for 1600 EST 22 April 2015

Of course, we don’t have good databases of cyclones in southern Australia to compare this easily to past events, so we have to make one. To do that, I ran the same cyclone tracking method I use for ECLs, but changed the region I was looking at to cover the Adelaide region - 25-45°S, 125-145°E. I ran this on gridded pressure data from the NCEP1 reanalysis, which allows us to look at cyclones back to 1948, although it’s important to note there might be some issues in the pressure data, especially before satellites came in in 1979.

Because the reanalysis only has pressure data every 250 km, the central pressure of the cyclone wasn’t as strong - 977.7 hPa. But this was the deepest cyclone in the analysis north of 37.5°S, which ties in pretty well to the 1-in-50 years claim. Deeper cyclones are more likely to be further south - some cyclones around Antarctica in my dataset had pressures down to 930 hPa!

2. Pressure gradients

The problem with using the central pressure of the cyclone is that pressure tends to get lower as you move south. This means that central pressure really isn’t a good indicator of how bad a cyclone’s impacts are, especially for East Coast Lows. The complex ECL that caused a lot of damage in June this year had a central pressure of 990 hPa; the severe ECL from April 2015 only got down to 1007 hPa.

So a more common way scientists will look at cyclone intensity is by looking at how strong the gradient of pressure is, which is a better indicator of how strong the winds will be. This can be measured in a number of ways, including by looking at the average difference in the pressure between the cyclone centre and the pressure 200km or 500km away, or by calculating metrics like the “Laplacian” of pressure (which is what my method uses).

Using this method, I get a Laplacian for the Adelaide cyclone of 3.5, which is way higher than the previous record for this far north in the Adelaide area of 2.9. This is also stronger than any of the ECLs in my database, although there have been a few with intensities stronger than 3, most recently in June 1998.

Of course, this is usually calculated as an average around the cyclone - ECLs often have much stronger gradients to the south of the cyclone than the north, so you may want to only calculate the maximum pressure gradient of the storm, or the gradient on the south side, to really get at those big events. And low-resolution pressure fields like in the NCEP reanalysis really can’t capture the smaller “mesolows” that cause locally severe impacts inside the bigger system. So the Laplacian for the April 2015 ECL is only 1.4, and the infamous Pasha Bulker storm is only 1.6.

3. Impacts

What people really care about with cyclones are the impacts - how strong was the wind, how much rain fell, how big were the waves? This can be influenced by a number of things beyond the cyclone itself, like how high the astronomical tides are or how warm/moist the atmosphere is (both important for the ECL in June 2016). It can also depend on other things like how big the cyclone is, how close it is to populated areas, or how long it stays in one place. These are even harder to quantify, although there is a good analysis looking at severe ECLs in terms of their coastal flooding.

Because the storm in September was that bit further north, it was in just the right place to have major impacts, with very strong winds and many September rainfall records. A few degrees further south, and you have the cyclones that passed through on 10 July and 18 August this year - South Australia still received strong winds and rain from the cold fronts attached, but the impacts were much smaller.

MSLP chart for 1600EST on 18 August 2016

So really, whatever way you look at it, the cyclone in South Australia last month looks like the strongest in the area for at least 60 years. But it’s not always as simple, especially when dealing with ECLs.